Rescale Input Feature in a Neural Network

Question

I'm reading through the Make Your Own Neural Network book and in the example where it is shown on how to classify the hand written digits, the text says that the input color values that are in the range from 0 to 255 will be rescaled to the much smaller range between 0.01 to 1.0. A few questions on this!

What is against using the actual range which is 0 to 255? What would rescaling bring me?
Does this mean that if I rescale my training set, train my model with this rescaled data, I then should also use a rescaled test data?

Any arguments please?

Utku Savaş · Accepted Answer · 2019-11-19 18:55:22Z

1

Rescaling the data will lead to faster convergence when using methods like gradient descent. Also when your dataset features highly varying in magnitudes, using solution that includes eucliden distance can lead to bad results. In order to avoid it, scaling the features to range between 0.0 and 1.0 will be a wise solution.

For the second question, you should rescale test data.

Click those links 1, 2 and 3 to obtain more information.

answered Nov 19, 2019 at 18:55

Utku Savaş

365 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Rescale Input Feature in a Neural Network

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related